previsioni di fonti rinnovabili non programmabili un
play

Previsioni di fonti rinnovabili non programmabili: un modello - PowerPoint PPT Presentation

Oct 14, 2022 •332 likes •945 views

Mercati energetici e metodi manuele.aufiero@milanomultiphysics.com quantitativi nicholas.bonfanti@milanomultiphysics.com 18 ottobre 2018 Previsioni di fonti rinnovabili non programmabili: un modello integrato per l'idroelettrico ad acqua

  1. Mercati energetici e metodi manuele.aufiero@milanomultiphysics.com quantitativi nicholas.bonfanti@milanomultiphysics.com 18 ottobre 2018 Previsioni di fonti rinnovabili non programmabili: un modello integrato per l'idroelettrico ad acqua fluente Milano Multiphysics -- ENTSO-E Smart solutions for complex problems

  2. Main goals of the developed approach ➔ Assess suitability of SMHI data for the production of hydro databases ➔ Investigate the possibility of modeling hydro plant inflows without geographic information ➔ Study an automated procedure for automated regression of RoR inflow ➔ Generation of synthetic hydro time series for adequacy studies MMP - ENTSO-E SMHI data for Hydro database

  3. Main goals of the developed approach ➔ Perform additional analyses: ◆ Automatic identification of regulated inflows impact on RoR ◆ Non-linear correction of inflow/power curve ◆ Robust detection of plant maintenance effects MMP - ENTSO-E SMHI data for Hydro database

  4. Main steps of the statistical analysis ➔ Pre processing ◆ Selection of complete datasets of plant production data MMP - ENTSO-E SMHI data for Hydro database

  5. Considered datasets: RoR plants (Italy) MMP - ENTSO-E SMHI data for Hydro database

  6. Main steps of the statistical analysis ➔ Pre processing ◆ Selection of complete datasets of plant production data ◆ SMHI inflow data normalization MMP - ENTSO-E SMHI data for Hydro database

  7. Considered datasets: SMHI inflows (Italy) MMP - ENTSO-E SMHI data for Hydro database

  8. Main steps of the statistical analysis ➔ Pre processing ◆ Selection of complete datasets of plant production data ◆ SMHI inflow data normalization ◆ Preliminary verification of the SMHI/hydro spatial correlation MMP - ENTSO-E SMHI data for Hydro database

  9. Verification of the SMHI/hydro correlation Simple sanity check for high quality RoR plant dataset with high correlation Case study: Italy Testing SMHI data for hydro database

  10. Italy - NORD SMHI/RoR correlation Zone-wise aggregated RoR correlation MMP - ENTSO-E SMHI data for Hydro database

  11. Main steps of the statistical analysis ➔ Pre processing ➔ Dimensionality reduction ◆ Proper orthogonal decomposition of SMHI inflow data Reduction of the input dimensionality from ~1000 to ~50-150 variables ➔ Drastic reduction of cpu requirement ➔ Avoid regression overfitting MMP - ENTSO-E SMHI data for Hydro database

  12. SVD of SMHI inflow data Derivation a reduced set of input variables as linear combination of original SMHI inflows MMP - ENTSO-E SMHI data for Hydro database

  13. Model order reduction (SVD) of SMHI data Case study: Italy Testing SMHI data for hydro database

  14. Model order reduction (SVD) of SMHI data Case study: Italy Testing SMHI data for hydro database

  15. Model order reduction (SVD) of SMHI data Case study: Italy Testing SMHI data for hydro database

  16. Model order reduction (SVD) of SMHI data Case study: Italy Testing SMHI data for hydro database

  17. Model order reduction (SVD) of SMHI data Case study: Italy Testing SMHI data for hydro database

  18. Main steps of the statistical analysis ➔ Pre processing ➔ Dimensionality reduction ➔ Transfer function derivation ◆ Plan by plant SMHI/hydro power regression by least squares minimization MMP - ENTSO-E SMHI data for Hydro database

  19. Plant-by-plant SMHI/power transfer function MMP - ENTSO-E SMHI data for Hydro database

  20. Main steps of the statistical analysis ➔ Pre processing ➔ Dimensionality reduction ➔ Transfer function derivation ◆ Plan by plant SMHI/hydro power regression by least squares minimization ◆ p-value based elimination of least significant regressors MMP - ENTSO-E SMHI data for Hydro database

  21. p-value based regression simplification Output example of the linear fit function, removing the least significant regressors. MMP - ENTSO-E SMHI data for Hydro database

  22. Main steps of the statistical analysis ➔ Pre processing ➔ Dimensionality reduction ➔ Transfer function derivation ➔ Post-processing ◆ Identification of spurious RoR (influenced by regulated hydro) MMP - ENTSO-E SMHI data for Hydro database

  23. Fourier analysis The production data has been analysed with a Fourier transform to show its spectrum. ➔ Yearly dynamics ➔ Seasonal dynamics ➔ Weekly dynamics (expected for regulated plants, but not for “true” RoRs) MMP - ENTSO-E SMHI data for Hydro database

  24. Identification of spurious RoR Fourier transform of hydro power production MMP - ENTSO-E SMHI data for Hydro database

  25. Identification of spurious RoR Fourier transform of hydro power production MMP - ENTSO-E SMHI data for Hydro database

  26. Identification of spurious RoR Fourier transform of hydro power production MMP - ENTSO-E SMHI data for Hydro database

  27. Identification of spurious RoR Fourier transform of hydro power production MMP - ENTSO-E SMHI data for Hydro database

  28. Main steps of the statistical analysis ➔ Pre processing ➔ Dimensionality reduction ➔ Transfer function derivation ➔ Post-processing ◆ Identification of spurious RoR ◆ Zone-wise aggregation of RoR power ◆ Regression validation with independent input dataset MMP - ENTSO-E SMHI data for Hydro database

  29. Validation set Part of the data is not used in the regressor training. This data is used to validate the regressors and check the presence of overfitting. MMP - ENTSO-E SMHI data for Hydro database

  30. Italy - NORD - SMHI/RoR correlation MMP - ENTSO-E SMHI data for Hydro database

  31. Italy - NORD - RoR Model verification MMP - ENTSO-E SMHI data for Hydro database

  32. Italy - NORD - RoR Model verification MMP - ENTSO-E SMHI data for Hydro database

  33. Italy - NORD - Past climatic year evaluation MMP - ENTSO-E SMHI data for Hydro database

  34. Italy - NORD - RoR statistics MMP - ENTSO-E SMHI data for Hydro database

  35. Italy - NORD - ``Basin´´ statistics* The subdivision in ``Basin´´ and ``Reservoir´´ plants was provided by TERNA in the input data MMP - ENTSO-E SMHI data for Hydro database

  36. Italy - NORD - ``Reservoir´´ statistics The subdivision in ``Basin´´ and ``Reservoir´´ plants was provided by TERNA in the input data MMP - ENTSO-E SMHI data for Hydro database

  37. Italy - NORD - Total storage statistics MMP - ENTSO-E SMHI data for Hydro database

  38. Italy - CENTRO SUD - RoR Model verification MMP - ENTSO-E SMHI data for Hydro database

  39. Italy - RoR aggregated results MMP - ENTSO-E SMHI data for Hydro database

  40. Italy - CSUD - Total storage statistics MMP - ENTSO-E SMHI data for Hydro database

  41. Italy - Mean Regression Errors Mean daily RoR power errors: - Italy: 0.168 GW [4.0% 3.2%] - NORD: 0.161 GW [4.3% 3.8%] - CSUD: 0.0199 GW [3.7% 2.5%] Mean weekly Storage power errors: - Italy: 0.135 GW [3.0% 1.8%] - NORD: 0.128 GW [3.0% 2.2%] - CSUD: 0.0172 GW [3.0% 2.3%] [absolute error / maximum power] [absolute error / installed power] MMP - ENTSO-E SMHI data for Hydro database

  42. France - Blind test This reduced analysis on France hydro data has been performed in collaboration with RTE thanks to the invaluable support of Frédéric Bréant and Pierre Goutierre For testing purposes, automatic regression on confidential, non-disclosable RoR time series has been kindly performed by RTE adopting the algorithm code provided by Milano Multiphysics. No data or hydro plant information were provided to MMP or ENTSO-E by RTE for this test. The automatic regression code only produced graphical, aggregated results for testing scopes. MMP - ENTSO-E SMHI data for Hydro database

  43. France - Blind test - Zone 5 MMP - ENTSO-E SMHI data for Hydro database

  44. France - Blind test - Zone 7 MMP - ENTSO-E SMHI data for Hydro database

  45. France - Blind test - Zone 9 MMP - ENTSO-E SMHI data for Hydro database

  46. Conclusion ➔ SMHI inflow data proved to be a good dataset for the modeling of most RoR and Storage plants ➔ The developed modeling approach allows for the derivation of reliable and accurate transfer functions ➔ France ``blind´´ test confirms the possibility to adopt the procedure without geographical information on hydro plants ➔ Developed transfer functions can be applied to reanalysis climate data to provide synthetic hydro time series (correlated with other climatic variables) for adequacy studies MMP - ENTSO-E SMHI data for Hydro database

  47. Parallel activities and ongoing development ➔ Extension of the approach to produce hydro time series databases for all ENTSO-E members ➔ Adoption of derived transfer functions for short-term (week ahead) RoR hydro forecast with robust uncertainty estimates MMP - ENTSO-E SMHI data for Hydro database

  48. Parallel activities and ongoing development ➔ Extension of the approach to produce hydro time series databases for all ENTSO-E members ➔ Adoption of derived transfer functions for short-term (week ahead) RoR hydro forecast with robust uncertainty estimates Thank you for the attention! MMP - ENTSO-E SMHI data for Hydro database

  49. Backup MMP - ENTSO-E SMHI data for Hydro database

More recommend